Some novel techniques of parameter estimation for dynamical models in biological systems

2011 ◽  
Vol 78 (2) ◽  
pp. 235-260 ◽  
Author(s):  
F. Liu ◽  
K. Burrage ◽  
N. A. Hamilton
Keyword(s):  
Parameter Estimation ◽  
Biological Systems ◽  
Dynamical Models

scholarly journals On Robust State Estimation of Gene Networks

2010 ◽  
Vol 2 ◽  
pp. 117959721000200 ◽  
Author(s):  
Chia-Hua Chuang ◽  
Chun-Liang Lin
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Quantitative Analysis ◽  
State Estimation ◽  
Gene Networks ◽  
Gene Network ◽  
Biological Systems ◽  
Uncertain Process ◽  
Network Systems ◽  
Internal States

Gene networks in biological systems are not only nonlinear but also stochastic due to noise corruption. How to accurately estimate the internal states of the noisy gene networks is an attractive issue to researchers. However, the internal states of biological systems are mostly inaccessible by direct measurement. This paper intends to develop a robust extended Kalman filter for state and parameter estimation of a class of gene network systems with uncertain process noises. Quantitative analysis of the estimation performance is conducted and some representative examples are provided for demonstration.

scholarly journals deBInfer: Bayesian inference for dynamical models of biological systems in R

2016 ◽  
Vol 8 (4) ◽  
pp. 511-518 ◽  
Author(s):  
Philipp H. Boersch‐Supan ◽  
Sadie J. Ryan ◽  
Leah R. Johnson
Keyword(s):  
Bayesian Inference ◽  
Biological Systems ◽  
Dynamical Models

scholarly journals PDEparams: Parameter fitting toolbox for partial differential equations in Python

2019 ◽  
Author(s):  
César Parra-Rojas ◽  
Esteban A. Hernandez-Vargas
Keyword(s):  
Parameter Estimation ◽  
Partial Differential Equations ◽  
Differential Equations ◽  
Biological Systems ◽  
Source Code ◽  
Parameter Analysis ◽  
Partial Differential ◽  
Parameter Fitting ◽  
Link Type ◽  
Boot Strapping

AbstractMotivationPartial differential equations (PDEs) is a well-established and powerful tool to simulate multi-cellular biological systems. However, available free tools for validation against data are not established. ThePDEparamsmodule provides flexible functionality in Python for parameter estimation in PDE models.ResultsThePDEparamsmodule provides a flexible interface and readily accommodates different parameter analysis tools in PDE models such as computation of likelihood profiles, and parametric boot-strapping, along with direct visualisation of the results. To our knowledge, it is the first open, freely available tool for parameter fitting of PDE models.Availability and implementationThePDEparamsmodule is distributed under the MIT license. The source code, usage instructions and step-by-step examples are freely available on GitHub atgithub.com/systemsmedicine/[email protected]

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